Data Science Immersive 2 at GA
Data Science Immersive 2 : March 20  June 9 2017

As the lead instructor, I am responsible for organizing and disseminating all the course material as well as creating new material as needed and managing the students and their capstone projects.

In this course, I taught basic Data Science with some advanced techniques. Some of the topics I teach in this course are: Git, Python, Data Structures, Inferential and Descriptive Statistics, Probability, EDA processes, numpy, visualization, SQL, Tableau, Decision Trees and Random Forest, Machine Learning, Classification (KNN, logistic, etc), Clustering (kmeans vs DBscan), Regression (polynomial, logistic, etc.), hypothesis testing, bootstrapping, PCA and NLP, to name just a few. The challenge of disseminating all data science materials across groups and individuals was met by combining Jupyter Notebooks demonstrating code with .py files and interactive materials (workshops) as well as talks and gamification of coding concepts.

Per GA requirements files were only hosted on GA github, for the full course description see: Data Science Course